With all aforementioned steps, we formulate the final HALP as follows. The pruning process takes a trained network as input and prunes it iteratively to satisfy the requirement of a given latency budget \(C\) . We perform one pruning every \(r\)  minibatches and repeat it \(k\)  pruning steps in total. In particular, we set \(k\)  milestones gradually decreasing the total latency to reach the goal via exponential scheduler [1]}, with \(C^1 > C^2 > \cdots > C^k\) , \(C^k=C\) . The algorithm gradually trims down neurons using steps below:
